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Early evaluations of multiple sclerosis (MS) with diffusion imaging focused on the most basic form of diffusion imaging, diffusion-weighted imaging (DWI), which is relatively easy to perform using only acquisitions in cardinal imaging planes. Diffusion tensor imaging (DTI) is employed to evaluate MS. The simplest non-spherical model of a full three-dimensional diffusion profile is described by a second-rank tensor. Useful mathematical parameters that emerge from the tensor include the geometric axes of the tensor ellipsoid, the mean diffusivity (MD), fractional anisotropy (FA), and the parallel and perpendicular diffusivity. Several investigators have proposed evaluating diffusion imaging data using voxel-wise comparisons (VWC). A new VWC method to address the issues of different smoothing algorithms is tract-based spatial statistics (TBSS). DTI is unique in its ability to identify white matter pathways in the brain. A recent study has demonstrated a high level of sensitivity for detection of disease progression in MS using DTI.